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Randomized Controlled Trial
, 5, 30

Genomic Signatures for Predicting Survival and Adjuvant Chemotherapy Benefit in Patients With Non-Small-Cell Lung Cancer

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Randomized Controlled Trial

Genomic Signatures for Predicting Survival and Adjuvant Chemotherapy Benefit in Patients With Non-Small-Cell Lung Cancer

Ryan K Van Laar. BMC Med Genomics.

Abstract

Background: Improved methods are needed for predicting prognosis and the benefit of delivering adjuvant chemotherapy (ACT) in patients with non-small-cell lung cancer (NSCLC).

Methods: A novel prognostic algorithm was identified using genomic profiles from 332 stage I-III adenocarcinomas and independently validated on a separate series of 264 patients with stage I-II tumors, compiled from five previous studies. The prognostic algorithm was used to interrogate genomic data from a series of patients treated with adjuvant chemotherapy. Those genes associated with outcome in the adjuvant treatment setting, independent to prognosis were used to train an algorithm able to classify a patient as either a responder or non-responder to ACT. The performance of this signature was independently validated on a separate series of genomic profiles from patients enrolled in a randomized controlled trial of cisplatin/vinorelbine vs. observation alone (JBR.10).

Results: NSCLC patients exhibiting the high-risk, poor-prognosis form of the 160-gene prognosis signature experienced a 2.80-times higher rate of 5-year disease specific death (log rank P < 0.0001) compared to those with the low-risk, good prognosis profile, adjusted for covariates. The prognosis signature was found to especially accurate at identifying early stage patients at risk of disease specific death within 24 months of diagnosis when compared to traditional methods of outcome prediction.Separately, NSCLC patients with the 37-gene ACT-response signature (n = 70, 64 %), benefited significantly from cisplatin/vinorelbine (adjusted HR: 0.23, P = 0.0032). For those patients predicted to be responders, receiving this form of ACT conferred a 25 % improvement in the probability of 5-year-survival, compared to observation alone and adjusted for covariates. Conversely, in those patients predicted to be non-responders, ACT was observed to offer no significant survival benefit (adjusted HR: 0.55, P = 0.32).The two gene signatures overlap by one gene only SPSB3, which interacts with the oncogene MET. In this study, higher levels of SPSB3 which were associated with favorable prognosis and benefit from ACT.

Conclusions: These complimentary prognostic and predictive gene signatures may assist physicians in their management and treatment of patients with early stage lung cancer.

Figures

Figure 1
Figure 1
Schematic diagram of datasets used to form training and validation series used in this study. Data from treatment-naïve adenocarcinoma patients enrolled in the NIH Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma were first used to develop a prognostic signature able to predict DSS, independent to clinical factors such as age and clinical stage [10]. This signature was validated on the independent adenocarcinoma series listed and then used to identify a new set of genes from ACT-treated patients that were associated with outcome, independent to prognosis. The second algorithm (ACT-response) was validated on data from Zhu et al. [8].
Figure 2
Figure 2
Association between the 160-gene prognostic signature, clinical and survival information in 301 untreated lung adenocarcinoma patients from Training Series A patients with at least 12 months follow-up). (A) Prognostic indexes range from −2 to +2 and are associated with an increase in DSS events, as indicated with a black line at (B). (C) Median-centered 160-gene expression profile used to compute the prognostic index (red = relative high expression, green = relative low expression). Each gene in the signature was chosen based on its statistically significant association with outcome, independent to age, stage, grade, gender and smoking history.
Figure 3
Figure 3
Kaplan Meier analysis of Validation Series A patients, stratified by gene expression risk group (A) and clinical stage (B). Kaplan Meier analysis was also performed on Stage IA patients from Validation Series A Stage stratified by AJCC stage (C), a clinical algorithm based on tumor size and age (D) and the 160-gene signature (C) for comparison purposes. The gene expression signature is able to more accurately identify stage I patients at risk of death within the first 24 months following diagnosis compared with clinical stage or combined clinical age + tumor size algorithm.
Figure 4
Figure 4
Kaplan Meier analysis: 37-gene signature treatment response predictions for independent Validation Series B. Patients in (A) Predicted ‘ACT-responder’ group exhibit significantly improved rate of DSS when treated with ACT compared to OBS alone. Patients in (B) Predicted ‘ACT non-responder’ group do not exhibit a significant difference in DSS between either treatment arm of the trial. Multivariate Cox Proportional Hazard analysis included age, gender, stage, NSCLC histological subtype and treatment (ACT or OBS).

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